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Experimentation for Revenue Growth: Driving Higher Average Order Values with Data-Backed Strategies

Experimentation for Revenue Growth: Driving Higher Average Order Values with Data-Backed Strategies

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A successful growth strategy in e-commerce has to go far beyond getting millions of visitors to a website or simply increasing conversions. It’s about making the most out of every interaction and ensuring you maximize the value of each and every conversion. This is why customer experience optimization is rapidly becoming a key driver to increase the average order value of any e-commerce transaction.


How to increase average order value? Every step matters.

Driving up the value of every online purchase is a strategy tied to improving the lifetime value of customers (LVC). This can only be achieved when companies understand what their customers want, what offerings are relevant to them, and when to serve additional options in the purchase flow. The ever persistent challenge comes from the fact that even if companies have well defined customer profiles, this information isn’t cast in stone. There are many variables that influence purchasing decisions which have to be taken into consideration when designing an effective customer journey. Being able to experiment and find which variations make the most impact requires a solution that can keep pace with changes as they occur and even start to autonomously consider different combinations.. The ability to zoom in and see which ideas get the best response and which combinations result in lift is vital when optimizing to increase AOV. 


Optimizing for as many variations as possible

Looking to maximize the value from each purchase includes strategies such as driving up attachment rates of complementary products. But there are many variations that can influence whether e-commerce visitors will consider adding additional products to their cart. How these products are presented, which products are showcased, what elements of added value are included - all impact purchasing decisions. Optimizing for the best possible customer experience needs to be able to evaluate how visitors respond to different options which goes far beyond  looking at just A versus B.


Case Study—How does increasing AOV fit into the bigger picture? 

A recent optimization for an e-commerce retailer revealed just how differently website users respond to ideas. Highlighting the importance of being able to experiment with as many ideas and combinations as possible. The project focused on optimizing two pages in the visitor journey, the product description page (PDP) and the shopping cart for both mobile and desktop users. The optimization showed that while similar changes were made to both pages, mobile and desktop users responded very differently. In some cases, the preferences highlighted by the best performing ideas were exactly opposite when on a mobile device compared to a desktop computer.

For mobile users, 25 unique ideas were implemented across 8 different elements of the PDP and cart pages. This resulted in 72,000 possible combinations. After only 30 days running the optimization, 6.6 million users were served up 141 unique experiences. This resulted in finding 7 top performing ideas and combinations, which achieved a 7% revenue lift. A key element of the optimization was determining the best way to present complementary offers with the objective of increasing the average order value as a secondary metric. This included experimenting with when and how the navigation behaved through checkout. It also included highlighting the various delivery options, adjusting the font and image size, as well as  the positioning of offers based on complementary product descriptions. 

For desktop users 30 unique ideas across 10 different elements were implemented. This created a massive number of 768,000 possible unique combinations. After only  30 days, 182 treatments were intelligently served up to users. During that time 10 best performing experiences were identified that resulted in an 8% revenue lift. How complementary products were presented, as well as progress through the customer journey contributed to an increase in average order value, which resulted in a significant increase in revenue generated.


Benefit from the ability to focus in and out on key elements

Being able to optimize to increaseAOV using so many unique experiences to different user segments has distinct benefits. It enables digital leaders to zoom in on secondary metrics while still aligning with the big picture view of what needs to be achieved.

Think of it as trying to photograph a bird in the wild. As beautiful and fascinating as birds are, they barely keep still for more than a split second. By nature, they’re always moving. So how does a photographer get the perfect shot? They combine specific photographic knowledge and real world experience with having the equipment that is fit for purpose. They have huge zoom lenses that enable them to get an extreme close up, know what settings to use and wait for just the right moment to take the shot.  

Getting a great photo of a bird in the wild is not that different from trying to identify the best customer experiences. Like a bird’s constant movements, the variables influencing a visitor’s decision to buy and add more to their cart, are always changing. Digital leaders need a solution that’s up to the task, that thrives on sorting through complexity and dynamic variables to find the best possible customer experiences. A solution that will not only achieve primary metrics such as growth, but also impact secondary metrics such as AOV. 

This is what AI-driven optimization is able to achieve. As demonstrated in the case study above, where Evolv’s client generated immediate revenue lift by focusing on a macro conversion objective while at the same time positively impacting a key secondary metric, in this case the AOV. Evolv’s AI also  identified all the elements that led to achieving that goal. This helps inform the next wave of optimizations and continue to serve a better digital customer experience.  

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